Communication theoretic image restoration for binary-valued imagery

We present a new image-restoration algorithm for binary-valued imagery. A trellis-based search method is described that exploits the finite alphabet of the target imagery. This algorithm seeks the maximum-likelihood solution to the image-restoration problem and is motivated by the Viterbi algorithm for traditional binary data detection in the presence of intersymbol interference and noise. We describe a blockwise method to restore two-dimensional imagery on a row-by-row basis and in which a priori knowledge of image pixel correlation structure can be included through a modification to the trellis transition probabilities. The performance of the new Viterbi-based algorithm is shown to be superior to Wiener filtering in terms of both bit error rate and visual quality. Algorithmic choices related to trellis state configuration, complexity reduction, and transition probability selection are investigated, and various trade-offs are discussed.     

Two-dimensional restoration of motion-degraded intensified CCD imagery

A Wiener filter-based deconvolution algorithm is developed to restore vibration-degraded video imagery from an intensified CCD camera. The method is based on the use of azimuth and elevation angular optical line-of-sight data recorded from external sensors to estimate a two-dimensional vibration-blur impulse response on a per frame basis. Flight conditions are reproduced in the laboratory by use of prerecorded in-flight vibration data. The performance of the algorithm varies from frame to frame, following the time-varying characteristics of the vibration-blur impulse response. However, real-time display of the restored video minimizes these effects because of eye integration, and near-full restoration of the original uncorrupted imagery is observed for both high-light- and low-light-level conditions with minimal amplification of noise.     

Improvements in SOVA-based decoding for turbo-coded storage channels

In this paper, we propose a novel and simple approach for dealing with the exaggerated extrinsic information produced by the soft-output Viterbi algorithm (SOVA). We first identify the reason behind these exaggerated values and then propose a simple remedy for it. We argue that what leads to this optimistic extrinsic information is the inherent strong correlation between the intrinsic information (input to the SOVA) and extrinsic information (output of the SOVA). Our proposed remedy is based on mathematical analysis, and it involves using two attenuators, one applied to the immediate output of the SOVA and another applied to the extrinsic information before it is passed to the other decoder (assuming iterative decoding). We examine the modified SOVA (MSOVA) on idealized partial response (PR) channels and the Lorentzian channel equalized to a PR target. We consider both parallel concatenated codes (PCCs) and serial concatenated codes (SCCs). We show that the MSOVA provides substantial performance improvements over both channels. For example, it provides improvements of about 0.8 to 1.6 dB at P/sub b/=10/sup -5/. Finally, we note that the proposed modifications, while they provide considerable performance improvements, introduce only two additional multipliers to the complexity of the SOVA algorithm, which is remarkable.     

Speckle reduction in optical coherence tomography by adaptive total variation method

An adaptive total variation method based on the combination of speckle statistics and total variation restoration is proposed and developed for reducing speckle noise in optical coherence tomography (OCT) images. The statistical distribution of the speckle noise in OCT image is investigated and measured. With the measured parameters such as the mean value and variance of the speckle noise, the OCT image is restored by the adaptive total variation restoration method. The adaptive total variation restoration algorithm was applied to the OCT images of a volunteer’s hand skin, which showed effective speckle noise reduction and image quality improvement. For image quality comparison, the commonly used median filtering method was also applied to the same images to reduce the speckle noise. The measured results demonstrate the superior performance of the adaptive total variation restoration method in terms of image signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, and mean square error.     

Multiaperture imaging

We study the reconstruction of a high-resolution image from multiple low-resolution images by using a nonlinear iterative backprojection algorithm. We exploit diversities in the imaging channels, namely, the number of imagers, magnification, position, rotation, and fill factor, to undo the degradation caused by the optical blur, pixel blur, and additive noise. We quantify the improvements gained by these diversities in the reconstruction process and discuss the trade-off among system parameters. As an example, for a system in which the pixel size is matched to the diffraction-limited optical blur size at a moderate detector noise level, we can reduce the reconstruction root-mean-square error by 570% by using 16 cameras and a large amount of diversity. The algorithm was implemented on a 56 camera array specifically constructed to demonstrate the resolution-enhancement capabilities. Practical issues associated with building and operating this device are presented and analyzed.     

Multiframe superresolution of binary images

We describe a new algorithm for superresolving a binary object from multiple undersampled low-resolution (LR) images that are degraded by diffraction-limited optical blur, detector blur, and additive white Gaussian noise. Two-dimensional distributed data detection (2D4) is an iterative algorithm that employs a message-passing technique for estimating the object pixel likelihoods. We present a novel non-training-based complexity-reduction technique that makes the algorithm suitable even for channels with support size as large as 5 x 5 object pixels. We compare the performance and computational complexity of 2D4 with that of iterative backprojection (IBP). In an imaging system with an optical blur spot matched to the object pixel size, 2 x 2 undersampled measurement pixels, and four LR images, the reconstruction error measured in terms of the number of pixel mismatches for 2D4 is 300 times smaller than that for IBP at a signal-to-noise ratio of 38 dB.     

Task-specific information for imaging system analysis

Imagery is often used to accomplish some computational task. In such cases there are some aspects of the imagery that are relevant to the task and other aspects that are not. In order to quantify the task-specific quality of such imagery, we introduce the concept of task-specific information (TSI). A formal framework for the computation of TSI is described and is applied to three common tasks: target detection, classification, and localization. We demonstrate the utility of TSI as a metric for evaluating the performance of three imaging systems: ideal geometric, diffraction-limited, and projective. The TSI results obtained from the simulation study quantify the degradation in the task-specific performance with optical blur. We also demonstrate that projective imagers can provide higher TSI than conventional imagers at small signal-to-noise ratios.     

Stream cipher based on pseudorandom number generation with optical affine transformation

We propose a new, to our knowledge, stream cipher technique for two-dimensional (2-D) image data that can be implemented by iterative optical transformation. The stream cipher uses a pseudorandom number generator (PRNG) to generate a pseudorandom bit sequence. The proposed method for the PRNG is composed of the iterative operation of 2-D affine transformation achieved by optical components and by modulo-n addition of the transformed images. We expect efficient execution of the method by optical parallel processing. We verify the performance of the proposed method in terms of security strength and clarify problems on optical implementation by the optical fractal synthesizer.     

Noise-cancellation-based nonuniformity correction algorithm for infrared focal-plane arrays

The spatial fixed-pattern noise (FPN) inherently generated in infrared (IR) imaging systems compromises severely the quality of the acquired imagery, even making such images inappropriate for some applications. The FPN refers to the inability of the photodetectors in the focal-plane array to render a uniform output image when a uniform-intensity scene is being imaged. We present a noise-cancellation-based algorithm that compensates for the additive component of the FPN. The proposed method relies on the assumption that a source of noise correlated to the additive FPN is available to the IR camera. An important feature of the algorithm is that all the calculations are reduced to a simple equation, which allows for the bias compensation of the raw imagery. The algorithm performance is tested using real IR image sequences and is compared to some classical methodologies.     

Super-resolution of images: Algorithms,principles, performance

A new facet of image restoration research has begun to emerge in recent years: super-resolution of images, which we define as the processing of an image so as to recover object information from beyond the spatial frequency bandwidth of the optical system that formed the image. Simple Fourier analysis would indicate that super-resolution is not possible. Therefore, it is important to reconcile this simplistic view with the existing algorithms that have been demonstrated to achieve super-resolution. In this article, we consider some of the algorithms that have demonstrated super-resolution and discuss the common principles that they share which makes it possible for them to recover some of the lost bandwidth of the object. We also consider the question of super-resolution performance, which is the measure of how much lost bandwidth can be recovered from a super-resolution algorithm, and how the performance is related to the algorithm principles that allow super-resolution to occur. We conclude with examples of super-resolution.     

Adaptive target detection with a polarization-sensitive optical system

We developed an adaptive polarimetric target detector (APTD) to determine the optimum combination strategy for a multichannel polarization-sensitive optical system. The proposed algorithm is based on scene-derived polarization properties of the target and background, and it seeks to find an optimum multichannel combination of linear polarizing filters that maximizes the signal-to-clutter ratio (SCR) in intensity and Stokes parameter images. The algorithm is validated by performing RX anomaly detection and a generalized likelihood ratio test on both synthetic and real imagery. The experimental results are analyzed through calculated SCR and receiver operating characteristic curves. Compared with several conventional operation methods, we find that better target detection performance is achieved with the APTD algorithm.     

Mathematical extrapolation of image spectrum for constraint-set design and set-theoretic superresolution

Several powerful iterative algorithms are being developed for the restoration and superresolution of diffraction-limited imagery data by use of diverse mathematical techniques. Notwithstanding the mathematical sophistication of the approaches used in their development and the potential for resolution enhancement possible with their implementation, the spectrum extrapolation that is central to superresolution comes in these algorithms only as a by-product and needs to be checked only after the completion of the processing steps to ensure that an expansion of the image bandwidth has indeed occurred. To overcome this limitation, a new approach of mathematically extrapolating the image spectrum and employing it to design constraint sets for implementing set-theoretic estimation procedures is described. Performance evaluation of a specific projection-onto-convex-sets algorithm by using this approach for the restoration and superresolution of degraded images is outlined. The primary goal of the method presented is to expand the power spectrum of the input image beyond the range of the sensor that captured the image.     

Fiber-image-guide-based bit-parallel optical interconnects

We propose a scalable bit-parallel optical interconnect method for use in large-bandwidth interprocessor communications. Flexible fiber image guides are used to transmit spatially parallel optical data between a vertical-cavity surface-emitting laser array and a photodetector array. We have studied a lens-based and a fiber-image-taper-based input-output coupling scheme and have modeled power-loss mechanisms and resolution-degradation mechanisms associated with the schemes. We have also performed some experiments to confirm the operational principles of the proposed schemes and to investigate the power efficiency and imaging-resolution performance of the interconnect schemes. Our study indicates that the proposed interconnects can offer a scalable method to transmit hundreds of channels of multigigabyte per second per channel optical data in parallel.     

Area-adaptive simulated annealing for image reconstruction and restoration

Simulated annealing (SA) is a robust, stable, but computationally costly method for solving ill-posed image-restoration problems. We describe the use of a backprojection operator that identifies those regions of an object estimate that have the greatest likelihood of being in error at each step of the SA process. This reduces computational time by concentrating the computing effort of SA on those pixels most effective in reducing the reconstruction error. The performance of an area-adaptive SA algorithm is evaluated for the restoration of images blurred by a simple pillbox space-invariant and a biconical space-variant point-spread function typical of a depth-measuring optical system.     

Signal detection for page-accessoptical memories with intersymbol interference

We propose a technique for data detection in a two-dimensional page-access optical memory. The technique combines sequence detection by the use of the Viterbi algorithm with decision feedback to improve the bit-error-rate performance in a system corrupted by intersymbol interference. It has an advantage in that it can be operated on a row-by-row basis as data are output from the optical detector. Use of the proposed scheme might ease the design tolerances of the optical components or permit the use of large data pages.     

General restoration filter for vibrated-image restoration

Mechanical vibrations are often the principal cause of image degradation. Low temporal-frequency mechanical vibrations involve random image degradation that depends on the instant of exposure. Exact restoration requires the calculation of a specific filter unique to each vibrated image. To calculate the restoration filter for each image, one needs the specific optical transfer function unique to the motion in the image. Therefore the instant of exposure and the motion function have to be measured or estimated by some other means. We develop a restoration filter for individual images blurred randomly by low-frequency mechanical vibrations. The filter is independent of the instant of exposure. The filter is designed to give its best performance averaged over a complete ensemble of vibrated images. Although when applying the new filter to any vibrated image the restoration achieved is slightly poorer than that achieved with an exact filter unique to the specific motion function, the new filter has the advantage of simplicity.     

Parallel detection algorithm for page-oriented optical memories

We present a parallel algorithm for the reliable detection of two-dimensional binary data in page-oriented memories. The development of the proposed pseudodecision-feedback equalization (PDFE) method is motivated by the classical decision-feedback equalization receiver. The technique takes advantage of the known or the estimated optical system characteristics to mitigate space-variant blur and additive thermal noise. We extend the method to correct for fixed-pattern errors including magnification, rotation, and transverse shift. Advantages of the PDFE algorithm include its parallel design, low computational complexity, and local connectivity. A system-capacity metric is used to compare the performance of the PDFE receiver with other conventional approaches, including the simple threshold, the 1:2 modulation code, and the Wiener filter. Results show the PDFE to outperform all the above techniques over a variety of channels for both incoherent and coherent systems. Implementation issues are discussed, and a MOSIS (Metal-Oxide Semiconductor Implementation Service) 2-mum design is presented.     

Efficient fringe image enhancement based on dual-tree complex wavelet transform

In optical phase shift profilometry (PSP), parallel fringe patterns are projected onto an object and the deformed fringes are captured using a digital camera. It is of particular interest in real time three-dimensional (3D) modeling applications because it enables 3D reconstruction using just a few image captures. When using this approach in a real life environment, however, the noise in the captured images can greatly affect the quality of the reconstructed 3D model. In this paper, a new image enhancement algorithm based on the oriented two-dimenional dual-tree complex wavelet transform (DT-CWT) is proposed for denoising the captured fringe images. The proposed algorithm makes use of the special analytic property of DT-CWT to obtain a sparse representation of the fringe image. Based on the sparse representation, a new iterative regularization procedure is applied for enhancing the noisy fringe image. The new approach introduces an additional preprocessing step to improve the initial guess of the iterative algorithm. Compared with the traditional image enhancement techniques, the proposed algorithm achieves a further improvement of 7.2 dB on average in the signal-to-noise ratio (SNR). When applying the proposed algorithm to optical PSP, the new approach enables the reconstruction of 3D models with improved accuracy from 6 to 20 dB in the SNR over the traditional approaches if the fringe images are noisy.     

Photonic content-addressable memory system that uses a parallel-readout optical disk

We describe a high-performance associative-memory system that can be implemented by means of an optical disk modified for parallel readout and a custom-designed silicon integrated circuit with parallel optical input. The system can achieve associative recall on 128 × 128 bit images and also on variable-size subimages. The system's behavior and performance are evaluated on the basis of experimental results on a motionless-head parallel-readout optical-disk system, logic simulations of the very-large-scale integrated chip, and a software emulation of the overall system.